Paper Info
Title
Application Of Fuzzy Logic For Iot Node Elimination And Selection In Opportunistic Networks: Performance Evaluation Of Two Fuzzy-Based Systems
Abstract
Opportunistic Networks (OppNets) are a sub-class of DTN, designed as a specialized ad hoc network suitable for applications such as emergency responses. Unlike traditional networks, in OppNets communication opportunities are intermittent, so an end-to-end path between the source and the destination may never exist. Existing networks have already brought connectivity to a broad range of devices, such as hand held devices, laptops, tablets, PC, etc. The Internet of Things (IoT) will extend the connectivity to devices beyond just mobile phones and laptops, but to buildings, wearable devices, cars, different things and objects. One of the issues for these networks is the selection of the IoT nodes to carry out a task in OppNets. In this work, we implement two Fuzzy-Based Systems: Node Elimination System (NES) and Node Selection System (NSS) for IoT node elimination and selection in OppNets. We use three input parameters for NES: Node's Distance to Event (NDE), Node's Battery Level (NBL), Node's Free Buffer Space (NFBS) and four input parameters for NSS: Node's Number of Past Encounters (NNPE), Node's Unique Encounters (NUE), Node Inter Contact Time (NICT), Node Contact Duration (NCD). The output parameter is IoT Node Selection Possibility (NSP). The results show that the proposed systems make a proper elimination and selection decision for IoT nodes in OppNets.
Year
DOI
Venue
2021
10.1007/s11280-020-00835-6
WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS
Keywords
DocType
Volume
Internet of things, Opportunistic network, Delay tolerant networks, Fuzzy logic, IoT node elimination, IoT node selection
Journal
24
Issue
ISSN
Citations 
3
1386-145X
0
PageRank 
References 
Authors
0.34
0
6
Name
Order
Citations
PageRank
Miralda Cuka11812.97
Donald Elmazi215731.95
Makoto Ikeda31202207.10
Keita Matsuo421255.01
Leonard Barolli51179144.22
Makoto Takizawa63180440.50